---
pretty_name: Wikimedia Enterprise Structured Contents — cywiki_namespace_0
language:
- cy
license: cc-by-sa-4.0
task_categories:
- text-generation
- question-answering
- text-retrieval
tags:
- wikipedia
- wikimedia-enterprise
- structured-contents
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*.parquet
---

# cywiki_namespace_0

Structured Contents snapshot of `cywiki_namespace_0` from the
[Wikimedia Enterprise API](https://enterprise.wikimedia.com/docs/snapshot/),
repackaged as Parquet with a pinned schema.

The upstream Wikimedia Foundation dataset
([`wikimedia/structured-wikipedia`](https://huggingface.co/datasets/wikimedia/structured-wikipedia))
ships NDJSON which has known issues loading via
`datasets.load_dataset()` — see discussions
[#5](https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5),
[#15](https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/15),
[#16](https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/16).
This dataset is the same upstream content, normalised so
`load_dataset(...)` works without specifying a `Features` override.

## Source

- Upstream: [Wikimedia Enterprise Structured Contents API](https://enterprise.wikimedia.com/docs/snapshot/#structured-contents-snapshot-download-beta)
- Snapshot identifier: `cywiki_namespace_0`
- Format at source: `.tar.gz` containing sharded `.ndjson`
- Shards in this release: 1

## File layout

```
README.md                         ← this file
schema.json                       ← pinned Arrow schema (IPC + field-name index)
data/
  cywiki_namespace_0_0.parquet
  cywiki_namespace_0_1.parquet
  ... (1 files total)
```

One Parquet file per upstream NDJSON shard. Files are independently
downloadable, so consumers can parallelise downloads or pull only the
subset they need rather than the whole dataset.

## Loading the dataset

Every shard has a byte-identical embedded schema (alphabetised
recursively so struct field order is stable across shards). All of
these load without extra config:

```python
# Hugging Face datasets
from datasets import load_dataset
ds = load_dataset("VoeTheDon/testing-wiki-structured", split="train", streaming=True)

# Polars
import polars as pl
df = pl.read_parquet("hf://datasets/VoeTheDon/testing-wiki-structured/data/*.parquet")

# DuckDB
import duckdb
duckdb.sql(
    "SELECT name, url FROM 'hf://datasets/VoeTheDon/testing-wiki-structured/data/*.parquet' LIMIT 10"
)

# pyarrow
import pyarrow.dataset as pads
table = pads.dataset("hf://datasets/VoeTheDon/testing-wiki-structured/data/", format="parquet").to_table()
```

Four columns are stored as JSON-encoded strings (see *Schema notes*
below). Decode on read:

```python
import json

row = next(iter(ds))
sections = json.loads(row["sections"])      # list[dict]
infoboxes = json.loads(row["infoboxes"])    # list[dict]
tables = json.loads(row["tables"])          # list[dict]
for ref in row["references"]:
    ref_meta = json.loads(ref["metadata"])  # dict
```

If you need the canonical Arrow schema explicitly (e.g. for a
validation step in a downstream pipeline), it's published as
`schema.json` at the repo root in Arrow IPC format:

```python
import json, pyarrow as pa
payload = json.loads(open("schema.json").read())
schema = pa.ipc.read_schema(pa.py_buffer(bytes.fromhex(payload["arrow_ipc_hex"])))
```

## Schema notes

Four fields are JSON-encoded rather than stored as native Arrow structs:

| Field | Why |
|---|---|
| `sections` | Recursive `has_parts[].has_parts[]…` nesting reaches depth ~100 in real articles. Apache Arrow's C Data Interface caps struct recursion at 64 levels; `datasets.load_dataset()` round-trips schemas through that interface and rejects deeper structures. |
| `infoboxes` | Same recursive shape as `sections`. |
| `tables` | Shallower today but the same recursive structure — pre-emptively encoded so future upstream changes don't break the schema. |
| `references[].metadata` | Open dict of Wikipedia citation-template parameters (`rft.jtitle`, `chapter-url`, `first1`, positional `"1"`, …). Hundreds of ad-hoc keys appear across articles, producing a different inferred Arrow `struct` per shard, which breaks cross-shard schema unification. |

All other fields (`name`, `url`, `abstract`, `event`, `license[]`,
`version`, `image`, `main_entity`, `references[]` excluding metadata,
etc.) retain native Arrow struct/list types and are queryable without
decoding.

Struct field order is alphabetised recursively so every shard has a
byte-identical embedded schema — pyarrow's JSON inference otherwise
captures keys in encounter order, which makes Arrow treat
`struct<a, b>` and `struct<b, a>` as different types.

## Known limitations

- **Article categories are not included.** The upstream Structured
  Contents snapshot does not expose categories. If you need them,
  query the regular [WME Snapshots API](https://enterprise.wikimedia.com/docs/snapshot/)
  or the [MediaWiki API:Categories](https://www.mediawiki.org/wiki/API:Categories)
  endpoint.
- License passes through the upstream Wikipedia license
  (CC-BY-SA-4.0) for article text.
- This is a Beta-tier upstream snapshot — schema can change between
  WME releases. Each version of this dataset re-pins `schema.json`;
  consumers who want a stable contract should pin to a specific
  dataset revision.